feat(vvt): recipient-type classification + 3-section VVT table

Per user request: BMW (and others) put their own services AND external
vendors in the same cookie-policy widget. The VVT-Tabelle now groups
them by Art. 30(1)(d) DSGVO recipient category so the DSB can act on
the right buckets:

  - INTERNAL      — owner processing for itself ('BMW AG — XYZ')
  - GROUP_COMPANY — same brand family, different legal entity ('BMW Bank')
  - PROCESSOR     — Auftragsverarbeiter, AVV-pflichtig (Adobe, Akamai)
  - CONTROLLER    — independent / joint controller (Meta Pixel, Google
                    Ads, LinkedIn — they run their own profiles)
  - AUTHORITY     — government bodies (rare in cookies)
  - OTHER         — fallback

New module vendor_classifier.py:
- owner_from_url(url) — derive site-owner token (bmw.de -> 'BMW',
  mercedes-benz.de -> 'Mercedes-Benz')
- classify(name, category, owner) — strict 5-tier heuristic:
  * INTERNAL: vendor name first-token is '<Owner>' / '<Owner> AG' /
    '<Owner> SE' / '<Owner> GmbH' / '<Owner> AG & Co. KG'
  * GROUP_COMPANY: starts with '<Owner> ' but isn't '<Owner> AG'
  * CONTROLLER: matches a known joint-controller list (Meta, Google
    Ads, YouTube, LinkedIn Insight, TikTok, Pinterest, Taboola,
    Outbrain, Criteo, Twitter, Reddit, ...)
  * PROCESSOR: legal-form suffix in name (GmbH, AG, Inc., A/S,
    B.V., S.A., Ltd., LLC, ...)
  * OTHER: anything else

vendor_extractor.extract_vendors_from_payloads now takes owner_name:
- Passes it through to classify() for every extracted vendor record
- The route derives owner_name via _company_name_from_url(doc_entries)
- LLM-extracted vendors are classified the same way (so V3 fallback
  also produces tagged records)

agent_doc_check_extras.build_vvt_table_html rewritten:
- Buckets vendors by recipient_type
- Renders one section per non-empty bucket, in canonical order
  (RECIPIENT_TYPE_SECTIONS), each with section header + count + bad
  count + nested table
- Within each section: sorted by compliance_score ascending
- Response JSON cmp_vendors includes recipient_type so the frontend
  can later import per-category into the VVT module

Expected BMW result: ~60 INTERNAL rows (BMW AG own services),
~25 PROCESSOR rows (Adobe, Adform, Akamai, AWS, ...), ~5 CONTROLLER
rows (Meta Pixel, Google, LinkedIn, Pinterest, Outbrain, Taboola).
This commit is contained in:
Benjamin Admin
2026-05-17 12:31:49 +02:00
parent 6c7d4c7552
commit fab1e35847
4 changed files with 272 additions and 56 deletions
@@ -390,8 +390,13 @@ async def _run_compliance_check(check_id: str, req: ComplianceCheckRequest):
cookie_payloads.extend(e["cmp_payloads"])
if e.get("text"):
cookie_text = e["text"]
# Site-owner derived from the submitted URLs — drives the
# INTERNAL/GROUP_COMPANY classification of vendor records.
owner_name = _company_name_from_url(doc_entries) or ""
if cookie_payloads:
cmp_vendors = extract_vendors_from_payloads(cookie_payloads)
cmp_vendors = extract_vendors_from_payloads(
cookie_payloads, owner_name=owner_name,
)
# V3 fallback: no named CMP captured but we have substantive
# cookie text → ask Qwen/OVH to extract vendor list from the text.
# Skip on very short text (likely navigation) to save LLM cost.
@@ -399,8 +404,17 @@ async def _run_compliance_check(check_id: str, req: ComplianceCheckRequest):
from compliance.services.vendor_llm_extractor import (
extract_vendors_via_llm,
)
from compliance.services.vendor_classifier import classify
_update(check_id, "Vendor-Liste per LLM extrahieren...", 94)
cmp_vendors = await extract_vendors_via_llm(cookie_text)
# LLM path doesn't run through extract_vendors_from_payloads,
# so classify here.
for v in cmp_vendors:
v["recipient_type"] = classify(
vendor_name=v.get("name", ""),
category=v.get("category", ""),
owner_name=owner_name,
)
if cmp_vendors:
logger.info("VVT: %d vendors extracted, validating links",
len(cmp_vendors))